Apache HugeGraph-Computer is a comprehensive graph computing solution providing two complementary systems for different deployment scenarios:

• Vermeer (Go): High-performance in-memory computing engine for single-machine deployments
• Computer (Java): Distributed BSP/Pregel framework for large-scale cluster computing

graph TB
    subgraph HugeGraph-Computer
        subgraph Vermeer["Vermeer (Go) - In-Memory Engine"]
            VM[Master :6688] --> VW1[Worker 1 :6789]
            VM --> VW2[Worker 2 :6789]
            VM --> VW3[Worker N :6789]
        end
        subgraph Computer["Computer (Java) - Distributed BSP"]
            CM[Master Service] --> CW1[Worker Pod 1]
            CM --> CW2[Worker Pod 2]
            CM --> CW3[Worker Pod N]
        end
    end

    HG[(HugeGraph Server)] <--> Vermeer
    HG <--> Computer

    style Vermeer fill:#e1f5fe
    style Computer fill:#fff3e0

Vermeer is designed with a Master-Worker architecture optimized for high-performance in-memory graph computing:

graph TB
    subgraph Client["Client Layer"]
        API[REST API Client]
        UI[Web UI Dashboard]
    end

    subgraph Master["Master Node"]
        HTTP[HTTP Server :6688]
        GRPC_M[gRPC Server :6689]
        GM[Graph Manager]
        TM[Task Manager]
        WM[Worker Manager]
        SCH[Scheduler]
    end

    subgraph Workers["Worker Nodes"]
        W1[Worker 1 :6789]
        W2[Worker 2 :6789]
        W3[Worker N :6789]
    end

    subgraph DataSources["Data Sources"]
        HG[(HugeGraph)]
        CSV[Local CSV]
        HDFS[HDFS]
    end

    API --> HTTP
    UI --> HTTP
    GRPC_M <--> W1
    GRPC_M <--> W2
    GRPC_M <--> W3

    W1 -.-> HG
    W2 -.-> HG
    W3 -.-> HG
    W1 -.-> CSV
    W1 -.-> HDFS

    style Master fill:#e1f5fe
    style Workers fill:#f3e5f5
    style DataSources fill:#fff9c4

Component Overview:

┌─────────────────────────────────────────────────────────────┐
│                    HugeGraph Ecosystem                      │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────────┐  │
│  │   Hubble    │    │  Toolchain  │    │  HugeGraph-AI   │  │
│  │   (UI)      │    │   (Tools)   │    │  (LLM/RAG)      │  │
│  └──────┬──────┘    └──────┬──────┘    └────────┬────────┘  │
│         │                  │                    │           │
│         └──────────────────┼────────────────────┘           │
│                            │                                │
│                    ┌───────▼───────┐                        │
│                    │  HugeGraph    │                        │
│                    │   Server      │                        │
│                    └───────┬───────┘                        │
│                            │                                │
│         ┌──────────────────┼──────────────────┐             │
│         │                  │                  │             │
│  ┌──────▼──────┐    ┌──────▼──────┐     ┌─────▼─────┐       │
│  │  Vermeer    │    │  Computer   │     │   Store   │       │
│  │  (Memory)   │    │  (BSP/K8s)  │     │  (PD)     │       │
│  └─────────────┘    └─────────────┘     └───────────┘       │
└─────────────────────────────────────────────────────────────┘

For quick start and single-machine deployments, we recommend Vermeer:

# Pull the image
docker pull hugegraph/vermeer:latest

# Change config path in docker-compose.yaml
volumes:
      - ~/:/go/bin/config # Change here to your actual config path, e.g., vermeer/config

# Run with docker-compose
docker-compose up -d

# Download and extract (example for Linux AMD64)
wget https://github.com/apache/hugegraph-computer/releases/download/vX.X.X/vermeer-linux-amd64.tar.gz
tar -xzf vermeer-linux-amd64.tar.gz
cd vermeer

# Run master and worker
./vermeer --env=master &
./vermeer --env=worker &

See the Vermeer README for detailed configuration and usage.

For large-scale distributed graph processing on Kubernetes or YARN clusters, see the Computer README for:

• Prerequisites and build instructions
• Kubernetes/YARN deployment guide
• 45+ algorithm implementations
• Custom algorithm development framework

Features:

• In-memory optimized implementations
• REST API for algorithm execution
• Real-time result queries

Computer (Java) Algorithms: For Computer's 45+ algorithm implementations including distributed Triangle Count, Rings detection, and custom algorithm development framework, see Computer Algorithm List.

• ✅ Quick prototyping and experimentation
• ✅ Interactive analytics with built-in Web UI
• ✅ Graphs up to hundreds of millions of edges
• ✅ REST API integration requirements
• ✅ Single machine or small cluster with high-memory nodes
• ✅ Sub-second query response requirements

Performance: Optimized for fast iteration on medium-sized graphs with in-memory processing. Horizontal scaling by adding worker nodes.

• ✅ Billions of vertices/edges requiring distributed processing
• ✅ Existing Kubernetes or YARN infrastructure
• ✅ Custom algorithm development with Java
• ✅ Memory-constrained environments (auto disk spill)
• ✅ Integration with Hadoop ecosystem

Performance: Handles massive graphs via distributed BSP framework. Batch-oriented with superstep barriers. Elastic scaling on K8s.

• Project Homepage
• Vermeer Documentation
• Computer Documentation
• HugeGraph Documentation

1. hugegraph - Graph database core (Server + PD + Store)
2. hugegraph-toolchain - Graph tools (Loader/Hubble/Tools/Client)
3. hugegraph-ai - Graph AI/LLM/Knowledge Graph system
4. hugegraph-website - Documentation and website

Welcome to contribute to HugeGraph-Computer! Please see:

• How to Contribute for guidelines
• GitHub Issues for bug reports and feature requests

We recommend using GitHub Desktop to simplify the PR process.

Thank you to all contributors!

HugeGraph-Computer is licensed under Apache 2.0 License.

• GitHub Issues: Report bugs or request features
• Email: dev@hugegraph.apache.org (subscribe first)
• Slack: Join ASF HugeGraph channel
• WeChat: Scan QR code to follow Apache HugeGraph official account